一句话：提出基于染色体遗传理论的合成过采样方法 MAHAKIL，缓解软件缺陷数据类别不平衡并提升预测性能。
软件缺陷数据集常存在类别不平衡问题，影响缺陷预测模型性能。本文提出 MAHAKIL，一种基于染色体遗传理论的新型合成过采样方法。它将两个不同子类视为父母，生成继承双方不同特征的新实例，以增加数据分布多样性。作者在 PROMISE 仓库的 20 个缺陷数据集上，使用五种预测模型，将 MAHAKIL 与五种采样方法进行广泛比较。实验表明，MAHAKIL 改善了所有模型的预测性能，并在稳健统计检验下取得更好且更显著的 pf 值。
This study presents MAHAKIL, a novel and efficient synthetic over-sampling approach for software defect datasets that is based on the chromosomal theory of inheritance. Exploiting this theory, MAHAKIL interprets two distinct sub-classes as parents and generates a new instance that inherits different traits from each parent and contributes to the diversity within the data distribution. We extensively compare MAHAKIL with five other sampling approaches using 20 releases of defect datasets from the PROMISE repository and five prediction models. Our experiments indicate that MAHAKIL improves the prediction performance for all the models and achieves better and more significant pf values than the other oversampling approaches, based on robust statistical tests.
DOI 原文 ·
@inproceedings{paperbot4401,
title = {MAHAKIL},
author = {Kwabena E. Bennin and Jacky Keung and Passakorn Phannachitta and Akito Monden and Solomon Mensah},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3182520}
}